ARTIFICIAL NEURAL
NETWORKS
Introduction to Neural
Networks
Neural network
• It is an information processing
paradigm
• It is based on the way in which
biological nervous system works.
• It helps in processing information.
• e.g. ANN
Use of Neural Networks
• Remarkable ability to derive meaning
from complicated data.
• Used to extract patterns and detect
complex trends.
• It can be compared to an expert.
• Advantages
1. Adaptive learning
2. Self organisation
3. Real time operations
4. Fault tolerance via redundant
information coding.
Neural network versus
Conventional Computers
• Conventional computers use algorithmic
approach i.e. computer follows a set of
instructions in order to solve a problem
which is in a way limit to solving capability.
• Neural networks process information like our
brain does.
• Neural networks and conventional
computers are not in competition but
complement to each other.
Similarities between human
and Artificial Neurons
Learning of a Human Brain
• The structure of a Human Neuron is shown
below
When a neuron receives excitatory inputs
larger than inhibitory input it sends an
electrical activity down its axon to the
synapses and thus the communication
between various neurons exists.
From human neurons to artificial
neurons
• First we deduce essential features of
neurons and their interconnection.
• Secondly, we program a computer to
stimulate these features .
• Finally model achieved is a gross
idealisation of real networks of
neurons.
An Engineering Approach
Artificial Neuron
• It is a device with many inputs and one output.
• Two modes of operation
1. Training mode
2. Using mode
Firing Rule
• Important concept accounting for high
flexibility in neural network.
• Firing rule can be implemented using
hamming distance technique.
• Firing rule applied to a 3 - input neuron.
X1: 0 0 0 0 1 1 1 1
X2: 0 0 1 1 0 0 1 1
X3: 0 1 0 1 0 1 0 1
OUT: 0 0 0/1 0/1 0/1 1 0/1 1
• The truth table after generalisation :
X1: 0 0 0 0 1 1 1 1
X2: 0 0 1 1 0 0 1 1
X3: 0 1 0 1 0 1 0 1
OUT: 0 0 0 0/1 0/1 1 1 1
Pattern Recognition
• An important application of neural
networks
• can be implemented using a feed
forward neural network that has been
trained accordingly.
• Example: The figure is trained to recognize
the following patterns:
The truth table for 3-neurons after generalisatio
X11: 0 0 0 0 1 1 1 1
X12: 0 0 1 1 0 0 1 1
X13: 0 1 0 1 0 1 0 1
OUT: 0 0 1 1 0 0 1 1
Top neuron
X21: 0 0 0 0 1 1 1 1
X22: 0 0 1 1 0 0 1 1
X23: 0 1 0 1 0 1 0 1
OUT: 1 0/1 1 0/1 0/1 0 0/1 0
Middle neuron
X21: 0 0 0 0 1 1 1 1
X22: 0 0 1 1 0 0 1 1
X23: 0 1 0 1 0 1 0 1
OUT: 1 0 1 1 0 0 1 0
Bottom neuron
From the tables following
associations can be extracted
• Conclusion-The output is black and the total
output of the network is still in favour of the
“T” shape.
Architecture of Neural
Networks
Feed-forward Networks
• Allow the signal to travel in one direction.
• Are straight forward networks that associate
inputs with outputs.
• Extensively used in pattern recognition.
Feedback Networks
• Signal travel in both directions.
• Are dynamic in nature.
• Used to denote feedback connections in
single layer organisations.
Network Layers
• Three units-input, hidden, output.
• Activities of these units.
• Simple network is interesting because
of hidden layers.
• Single and multi-layer architectures.
Applications of Neural
Networks
Neural Networks in Practice
• They are best suited for prediction or
forecasting including: industrial
process control, data validation, risk
management, etc.
• Also used in specific paradigms:
interpretation of multi meaning, texture
analysis, facial recognition,
recognition of speakers in
communications ,etc.
Neural Networks in medicine
• The research on modeling parts of the
human body and recognizing diseases
from various scans.
• Used effectively in recognizing
diseases as no details are needed to
how to recognize the and no specific
algorithm need to be provided.
Modelling and diagonising the
cardiovascular system
• Potential harmful medical conditions
can be detected at early stage using
artificial cardiovascular system models.
• Ann technology is used as it provides
sensor fusion which is combining of
several values from different sensors
Electronic Noses
• Neural networks have made possible to
transmit various odours over long
distances via communication links.
• This has help in enhancing
telemedicine and telepresent surgery.
Instant Physician
• An associative neural network to store
a large number medical records
including symptoms,diagnosis,and
treatment of specific case.
• After training, the net can be presented
with input consisting of a set of
symptoms; it will then find the full
stored pattern that represents the
"best" diagnosis and treatment.
Neural Networks in Business
• Any neural network application would
fit into one business area or financial
analysis.
• Neural networks is used for dataminig
purposes, for various business
purposes including resource allocation
and scheduling.
Enhancing Trading
• The identification of specific patterns in
stock price derived from technical
stock analysis heuristics, which after
occurring results in a predefined price
movement.
• Neural networks are trained in the
experiments to classify whether the
outcome of an occurred pattern will
result in a predefined price movement.
ANNs in Water Supply
Engineering
• Whenever this technology is applied for
water supply engg. problems have reported
findings that were beyond the capability of
traditional statistical / mathematical
modeling tools.
• Some of the applications performed
includes: Forecasting salinity levels in River
Murray, South Australia; Predicting
gastroenteritis rates and waterborne
outbreaks; Modeling pH levels in a eutrophic
Middle Loire River, France;
Understanding Brain Activity
• It provides a powerful new approach for
neuroscience to study and manipulate signal
propagation in neuronal networks
• It represents a new, powerful, and flexible
approach for real-time cellular assays useful
for drug discovery and other applications;
and it opens the possibility for hybrid
circuits that couple the strengths of digital
nanoelectronic and biological computing
components.